![]() A visual explanation for regularization of linear models (May 2020) I provide full PyTorch implementation notebooks that use just linear algebra and the autograd feature. This articles explains RNNs without neural networks, stripping them down to its essence-a series of vector transformations that result in embeddings for variable-length input vectors. But, sometimes the neural network metaphor makes it less clear exactly what's going on. Vanilla recurrent neural networks (RNNs) form the basis of more sophisticated models, such as LSTMs and GRUs. Explaining RNNs without neural networks (July 2020) ![]() See also the TensorSensor implementation slides (PDF). It works with JAX, Tensorflow, PyTorch, and Numpy, as well as higher-level libraries like Keras and fastai. This article describes a new library called TensorSensor that clarifies exceptions by augmenting messages and visualizing Python code to indicate the shape of tensor variables. One of the biggest challenges when writing code to implement deep learning networks is getting all of the tensor (matrix and vector) dimensions to line up properly, even when using predefined network layers. Follow Articles Clarifying exceptions and visualizing tensor operations in deep learning code (October 2020) After 30 years of parsing, he's back to machine learning and really enjoys trying to explain complex topics deeply and in the simplest possible way. While he is best known for creating the ANTLR parser generator, Terence actually started out studying neural networks in grad school (1987). of San Francisco, where he was founding director of the MS in data science program in 2012. Terence Parr is a tech lead at Google and until 2022 was a professor of data science / computer science at Univ. Deep explanations of machine learning and related topics.
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